Integration Of Factor Analysis Research Articles

A factor analysis based automatic optimization method of vibration sensor points for ship acoustic reconstruction

To address the challenge associated with optimizing the number and positioning of accelerometer points in ship acoustic reconstruction, factor analysis is applied to the automatic optimization of accelerometer position in this work according to the source independence of ship’s underwater radiated noise. Based on the numerical simulation method that calculates the vibration noise response under specific operation conditions, the minimum number and the optimal position of the accelerometers can be obtained through the factor analysis and multi-parameter comprehensive evaluation method. The results of acoustic reconfiguration and testing are compared using one test cabin as a validation object to verify the effectiveness of the proposed method. The results show that the automatic optimization algorithm can improve the reconstruction accuracy concerning the uniform distribution method. The error is reduced from 15.46% (1.5 dB) to 8.35% (0.8 dB), and the correlation coefficient is improved from 0.86 to 0.89. The semi-automatic optimization method is slightly lower in terms of errors and correlation coefficients compared to the automatic optimization algorithm. After increasing the number of monitoring positions for automatic optimization, the acoustic reconstruction accuracy is further improved, while the average error is reduced to 7.37% (0.7 dB) and the correlation coefficient is improved from 0.89 to 0.92. The experimental results are proximate to the conclusions of the simulation calculation. Even under the circumstance of multi-factor noise interference, the correlation coefficient of multi-position automatic optimization can still reach 0.8, in comparison with the uniform distribution method, the error is reduced from 32.49% (3.4 dB) to 25.94% (2.6 dB). The proposed method effectively captures the dynamic characteristics of complex structures and their impact on underwater radiated noise, and the integration of factor analysis can prominently enhance the computational efficiency, thereby providing technical support for engineering applications to the ship acoustic reconstruction.

Enhancing effluent quality prediction in wastewater treatment plants through the integration of factor analysis and machine learning

Precisely predicting the concentration of nitrogen-based pollutants from the wastewater treatment plants (WWTPs) remains a challenging yet crucial task for optimizing operational adjustments in WWTPs. In this study, an integrated approach using factor analysis (FA) and machine learning (ML) models was employed to accurately predict effluent total nitrogen (Ntoteff) and nitrate nitrogen (NO3-Neff) concentrations of the WWTP. The input values for the ML models were honed through FA to optimize factors, thereby significantly enhancing the ML prediction accuracy. The prediction model achieved a highest coefficient of determination (R2) of 97.43 % (Ntoteff) and 99.38 % (NO3-Neff), demonstrating satisfactory generalization ability for predictions up to three days ahead (R2 >80 %). Moreover, the interpretability analysis identified that the denitrification factor, the pollutant load factor, and the meteorological factor were significant. The model framework proposed in this study provides a valuable reference for optimizing the operation and management of wastewater treatment.

Integration of factor analysis and Tsukamoto’s fuzzy logic method for quality control of credit provisions in rural banks

Giving credit to debtors can pose a default risk. This risk arises because of an error in analyzing the credit risk rate of the debtor. Therefore, this study aims to design a framework for analyzing the credit risk rate of debtors so that the default risk can be reduced. This framework is created using the integration of factor analysis and Tsukamoto’s fuzzy logic method. This integration method can group many credit assessment variables into several decisive factors. In addition, the integration method can estimate credit risk rate firmly based on the α-predicate of each basic rule. This analytical framework is simulated on credit application data at a Rural Bank, in Indonesia. The simulation results show that there are three factors and one variable to measure the credit risk rate, namely: factor 1 represents repayment capacity, business length, working capital, and liquidity value; factor 2 represents the age and the difference between the granted and the proposed loan amount; factor 3 represents the stay length, character, and credit history; and one variable represents a dependent number. This research is expected to help credit institutions measure the credit risk rate in making credit decisions for prospective debtors.

Application of factor analysis and concentration-volume fractal modeling to delineation of 3D geochemical patterns: a case study of the Jinwozi gold field, NW China

Delineation of vertical distribution patterns of elements in overburden over a concealed deposit is essential for determining potential upward migration mechanism of elements and for predicting undiscovered mineral deposits. The aim of this study is to identify the vertical distribution features of elements in the regolith over the Jinwozi gold field, NW China by using factor analysis (FA) and concentration-volume (C-V) fractal modeling based on the data-set generated from 70 overburden drilling boreholes. Firstly, the zones of different gold enrichment states were obtained by the C-V fractal method. Secondly, FA was utilized to define the mineralization-related element association. Finally, the C-V method was applied to the relevant association defined by FA. The results showed that: (1) the C-V fractal method distinguished four enrichment states determined by Au thresholds of 1.6, 7.9 and 39.9 ng/g and by factor scores of −1.14, 0.33 and 1.45; (2) the first factor that describes 41.5% of the common variance has positive loadings in Au, As, Sb and Mo; (3) the zones of both high Au contents and factor scores generally showed horizontally northeasterly distribution along the ductile-shearing alteration belt and vertically top-bottom symmetric distribution in the regolith over the deposit; (4) the rest of the zones determined by the C-V fractal model properly delineated the zones of low and high backgrounds and moderate Au enrichment; and (5) the moderately-enriched zones of the raw Au data are better than those of the factor scores, and the rest of the zones have the similar distributions. These results indicate that: (1) the distribution of elements in the regolith was the product of nonlinear natural processes resulting in an enrichment pattern at the surface over the deposit; and (2) the integration of FA and C-V fractal model is effective for the delineation of 3D geochemical patterns.

Factor analysis and geographic information system for determining probability areas of presence of illegal landfills

The objective of this study is to develop a methodology for determining areas in which there is a distinct probability of the presence of illegal landfills. This methodology is developed in three stages: (a) the application of factor analysis (FA) to identify relevant geographical factors (factor model); (b) the construction of a geostatistical model to calculate spatial patterns based on the identified factors; and (c) the integration of the geostatistical model into a geographic information system (GIS) to determine and locate the illegal landfill sites (spatial model).This methodology has proven to be valid because it confirmed that a verified population of illegal landfills (518) is not randomly distributed; instead, most of the illegal landfills (63.6%) are found in the areas of highest probability (over 36%). Additionally, the study confirmed that the application of this methodology (FA and GIS) provides adequate results at the regional and local level.The described method may also be applied to other spatial environments, as long as the necessary thematic and spatial data are available (although results would vary according to demographic, socioeconomic, geomorphological, and environmental management characteristics).Finally, the benefit of this methodology lies in the fulfilment of two necessary and sufficient demands. (a) The model does not arbitrarily include variables related to the probability of the presence of illegal landfills and considers those variables that have been shown via FA. (b) The variables included in the spatial model are not considered to have the same importance.Thus, the integration of FA and GIS offers an alternative tool to the application of multi-criteria evaluation as this approach determines the criteria and their relative weights based on substantiated and non-aprioristic indications. Moreover, the methodology used in this study enables the creation of models because the GIS makes an excellent platform for the development, application, and validation of these models.

Assessment of PM10 Enhancement by Yellow Sand on the Air Quality of Taipei, Taiwan in 2001

The impact of long-range transport of yellow sand from Asian Continent to the Taipei Metropolitan Area (Taipei) not only deteriorates air quality but also poses health risks to all, especially the children and the elderly. As such, it is important to assess the enhancement of PM(10) during yellow sand periods. In order to estimate PM(10) enhancement, we adopted factor analysis to distinguish the yellow-sand (YS) periods from non-yellow-sand (NYS) periods based on air quality monitoring records. Eight YS events were identified using factor analysis coupling with an independent validation procedure by checking background site values, examining meteorological conditions, and modeling air mass trajectory from January 2001 to May 2001. The duration of each event varied from 11 to 132 h, which was identified from the time when the PM(10) level was high, and the CO and NOx levels were low. Subsequently, we used the artificial neural network (ANN) to simulate local PM(10) levels from related parameters including local gas pollutants and meteorological factors during the NYS periods. The PM(10) enhancement during the YS periods is then calculated by subtracting the simulated PM(10) from the observed PM(10) levels. Based on our calculations, the PM(10) enhancement in the maximum hour of each event ranged from 51 to 82%. Moreover, in the eight events identified in 2001, it was estimated that a total amount of 7,210 tons of PM(10) were transported by yellow sand to Taipei. Thus, in this study, we demonstrate that an integration of factor analysis with ANN model could provide a very useful method in identifying YS periods and in determining PM(10) enhancement caused by yellow sand.

The integration of factor analysis with psychology; a reply to Professor Godfrey Thomson's review of "The Description and Measurement of Personality."

The integration of factor analysis with psychology; a reply to Professor Godfrey Thomson's review of "The Description and Measurement of Personality."